System and method for improved data consistency in data systems including dependent algorithms

ABSTRACT

A data system is provided for analyzing and maintaining data obtained from one or more data sources on which the data system depends. The system includes a primary database including current values used by the system and a collection of executable algorithms used to generate the data maintained in the primary database. In response to receiving a notification regarding a change in one of the data sources, a dependency database is used to establish an execution order for algorithms of the algorithm collection that are directly or indirectly dependent on the changed data. The algorithms identified in the execution order are then executed in accordance with the execution order and the corresponding result is stored in the primary database. The system may include data harvesters adapted to recognize changes in the data sources and to generate and transmit corresponding change notifications when such changes occur.

TECHNICAL FIELD

Improving data consistency in data systems implementing hierarchicaldata processing, including, but not limited to, data systems forreal-time scoring of customers and business opportunities.

BACKGROUND

Prioritizing resources within a company requires balancing pastperformance with future opportunities. For example, companies have tobudget resources for sales, marketing, R&D, engineering, etc., and,within those departments, must decide what people and resources will beassigned to particular projects and/or customers. Although companiesoften use data to allocate resources, many opportunities are notrealized because the opportunities are insufficiently prioritized orunderstood. One reason for this is that human beings often struggle tosimultaneously evaluate and process multiple layers of information.Moreover, scoring and evaluating data in real-time quickly becomessomething human beings are incapable of due to both the complexity ofthe evaluation and the speed required for generating a result within auseful timeframe. Finally, even if the data is understood, it often istoo complex to be useful for decision making.

It is with these observations in mind, among others, that variousaspects of the present disclosure were conceived and developed.

SUMMARY

Systems and methods in accordance with the present disclosure aregenerally directed to data processing systems for retrieving andanalyzing large quantities of data, such as, but not limited to consumeror business data obtained from third-party data vendors.

In one implementation, a method of updating and analyzing data of a datasystem is provided. The method includes receiving a change notificationindicating a change to a field data of a data source on which the datasystem relies. In response to the notification, a database (referred toherein as a “tier 1” database) used to store current data used by thedata system is updated to reflect the changed value. In addition toupdating the tier 1 database, algorithms of an algorithm collection thatare dependent (either directly or indirectly) on the changed data areidentified. The process of identifying the algorithms includesgenerating an execution order that specifies the order in which thealgorithms are to be executed in order to maintain data consistencywithin the system. The algorithms are then executed in accordance withthe execution order and the corresponding results are stored in the tier1 database.

The dependency data is stored in a dependency database, which may beimplemented as a graph database. Accordingly, certain implementationsmay include creating a dependency graph or database. The dependencydatabase generally describes the dependencies between the algorithms ofthe algorithm collection and the relationship between input and outputsof the algorithms included in the dependency database. The dependencydatabase may be used for generating a processing or execution order forthe algorithms based on the dependency information contained therein.For example, in certain embodiments, generating a processing orexecution order may first include identifying algorithms dependent onchanged data and then traversing the dependency database to identifyother algorithms that are indirectly dependent on the changed data. Theidentified algorithms may then be executed according to the executionorder such that data consistency is preserved.

In another implementation, a system for organizing and analyzing datafrom multiple data sources is provided. The system includes an algorithmcollection including a plurality of executable algorithms for evaluatingdata, a first database including dependency data for each algorithm ofthe plurality of algorithms and at least one computing device. Thecomputing device is configured to receive notifications corresponding tochanged data of data sources and, in response, execute one or more ofthe algorithms dependent on the changed data according to the dependencydata maintained in the first database. The computing device may thenupdate data stored in a second database to include the changed dataand/or the results of algorithms dependent on the changed data.

In certain embodiments, the at least one computing device receivesnotifications from data harvesting applications that are incommunication with the data sources. The data harvesters identifychanges in the data sources and generate notifications includingnormalized data obtained from the data source.

In other embodiments, the server may also be adapted to receive anexecution plan that includes an execution order for the algorithmsdependent on the changed data and to execute the algorithms according tothe execution order. For example, in certain embodiments, the server maygenerate and transmit a request for an execution plan to an applicationin communication with the first database and to receive the executionplan in response to the request.

In yet another implementation, a system for organizing and analyzingdata from multiple data sources is provided. The system includes analgorithm collection including executable algorithms and a dependencydatabase that includes dependency data for each algorithm of thealgorithm collection, the dependency data indicating data relationshipsbetween algorithms of the algorithm collection. The system furtherincludes a current database for storing current values of data stored inone or more data sources and results of algorithms of the algorithmcollection. The system also includes one or more computing devicescommunicatively coupled to the algorithm collection, the dependencydatabase, and the current database. The computing devices are configuredto recognize changes to data of the data sources, to identify algorithmsof the algorithm collection either directly or indirectly dependent onthe changed data and to generate execution orders using the dependencydata. The computing devices are further configured to generate algorithmresults by executing the identified algorithms according to theexecution order and updating the current database with the algorithmresults.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-B are example dependency graphs illustrating dependenciesbetween algorithms.

FIG. 2 is a schematic illustration of a data system in accordance withthe present disclosure.

FIGS. 3A-B are a flow chart illustrating a method of maintainingcustomer data that may be implemented in a data system, such as the datasystem of FIG. 2.

FIG. 4 is an example computing system that may implement various systemsand methods of the presently disclosed technology.

DETAILED DESCRIPTION

Described herein are systems and methods which allow for improvement inevaluating data in real-time. Such data evaluation techniques may beused in, but are not limited to, the evaluation of customers andbusiness opportunities.

Systems and methods in accordance with this disclosure rely on datastored within one or more data sources. Each data source includes datafields within which corresponding data values are stored and which maybe used by the system to evaluate how best to allocate businessresources. In certain implementations, for example, the stored datacorresponds to existing and/or potential customers and the evaluationdetermines the potential benefits of pursuing new business opportunitieswith corresponding customers.

Several technical features of implementations disclosed herein enablesuch analysis to be conducted with improved speed and efficiency ascompared to known systems while ensuring data integrity and consistency.Data analysis, including analysis of business data for purposes ofidentifying business opportunities, involves aggregating large amountsof data and performing many calculations and analyses on the aggregateddata. The amount of data and complexity of calculations makes timelymanual analysis by humans inefficient to the point of practicalimpossibility and, as a result, computing systems are routinelyimplemented for collecting, processing, and analyzing relevant data.

Known data analysis systems generally import large quantities of bulkdata from remote data sources and then analyze the imported data using apredetermined suite of routines and algorithms. Many known systemssuffer from various inefficiencies. For example, known systems oftenrequire that all data from a particular source be imported even if onlya portion of the data is relevant to the data analysis being performed.Moreover, even if known systems filter irrelevant data, all relevantdata is often imported despite the fact that only a small portion of therelevant data may have been modified since the most recent importation.As a result, the process of importing data in known systems generallymake inefficient use of network bandwidth and computational resourcesused for data importation, resulting in increased power consumption,computational time, and costs. Moreover, even after data importation,known systems generally require that the full suite of routines andalgorithms used in analyzing the data be executed instead of just theroutines and algorithms affected by changes to the underlying data.Again, this leads to inefficient use of computation resources andsubsequent increases in power consumption, wasted computational time,and related costs.

Known data systems are also generally unable to provide real-timeupdates due to the time and computational resources required forupdating data. More specifically, such updates are generally only runperiodically (e.g., every few days) and during off-peak times (e.g.,overnight). As a result, the accuracy of data used in known systems islimited by the frequency with which it is updated, and employees, suchas sales team members, are often required to rely on stale data whenevaluating business decisions.

In contrast to known systems, the methods and systems according to thepresent disclosure are realized by, among other things, limiting thequantity and frequency of data retrieved from external data sources andlimiting the computations performed against such data to only thosewhich are necessary to reflect additions or changes in the importeddata. These advantages are achieved, in part, by implementing dataharvesting applications adapted to monitor and communicate only relevantchanges to data sources to a data handling system for processing and bymaintaining a database of current values used in the evaluationsperformed by the system. To reduce computational workload, the systemfurther includes a collection of algorithms and dependency data for thealgorithms such that when a data change is identified by one of the dataharvesting applications, only those algorithms that are dependent on thechanged data may be identified and executed to update the valuesmaintained in the database. Accordingly, as compared to known systems,systems according to the present disclosure require, among other things,less bandwidth when importing data and less computational power toensure that any changes to relevant data are propagated through thesystem. Moreover, the system may employ a faster and smaller database,optimizing access and costs related to processing the high value data.As a result, systems according to the present disclosure reducebandwidth, power consumption, and overall costs in operating a dataanalysis system and improve the speed at which data within such systemsis updated, thereby enabling employees to have access to accurate andup-to-date data from which they can make informed business decisions.

In implementations of the present disclosure, a collection of algorithmsmay be maintained and used to perform various calculations andevaluations using data available to the system. Such data may includedata stored within the one or more data sources and retrieved usingcorresponding data harvesters, but may also include results fromexecuting algorithms of the algorithm collection. In certainimplementations, the values of data fields used by and/or generated byalgorithms of the algorithm collection may be stored within one or more“tier 1” databases for ease and speed of access. For example, the tier 1database may store data retrieved from the one or more data sources,results of algorithms of the algorithm collection, and current “scores”or other similar evaluation metrics produced by executing algorithms ofthe algorithm collection. By storing such data in the tier 1 database,the data can be accessed by the system without having to unnecessarilyretrieve the data from the data sources or re-execute correspondingalgorithms of the algorithm collection. For example, an inquiry into thestrength of a potential business opportunity may simply includeperforming a lookup or similar basic operation on the tier 1 databasefor a previously generated score as opposed to a more time- andresource-intensive process including importing data from a data sourceand/or performing a series of calculations.

By implementing the tier 1 database, a change in data of a data sourcemay be efficiently propagated through the system. More specifically, inresponse to receiving a notification including changed data of a datasource from a data harvester, the system identifies and re-executes onlythose algorithms that are directly or indirectly dependent on thechanged data. To the extent such algorithms are also dependent on otherdata (whether from a data source or a result of a second algorithmindependent of the changed data), the other data may be retrieved fromthe tier 1 database. Accordingly, only the minimum set of algorithms arerequired to be executed by the system, reducing the computationalworkload associated with propagating the change through the system andthe time required for making the updated data available for use.

As previously discussed, execution of algorithms of the algorithmcollection may result in the generation of a score or similar metricassociated with a particular business opportunity, customer, etc. Thescore could, for example, be a grade based on the weighted outputs ofsome or all of the algorithms such that the score reflects a weightedsum of the scores or outputs for each algorithm. Such scores can then besimplified even further, to letter grades or even binary (e.g.,pass/fail) grades. If, for example, an option ranked highly in multiplecategories, the system could provide an “A” or an “A-” score, whereas ifthe analyses indicated that an option was average or mediocre inmultiple categories, the option or might receive a “C” or “D” score.

Letter scores are just one way of scoring options and opportunities.Number rankings, such as “1 to 10” or “1 to 100”, “good or bad”, or anyother ranking system, are similarly possible. More generally, the scoreis intended to provide an overall evaluation of an option condensed downto a single point of evaluation, such as, but not limited to, a letteror number score. Scoring of a particular option may be facilitated bythe use of tables, weighting systems, equations, and/or any othermechanisms from which a score may be determined from multiple datapoints (which may be, among other things, outputs of algorithms or dataobtained from the one or more data sources).

Data consistency is often a critical aspect of complex data evaluationsystems. More particularly, such systems may perform evaluations thatrely on algorithms that depend on the results of other algorithms. As aresult, when data is modified within such systems, ensuring dataconsistency may require certain algorithms to be executed in aparticular order. For example, the output of a first algorithm may beused as input for a second algorithm, such that the second algorithm isdependent on the first algorithm. FIG. 1A is a first dependency graph100 illustrating such a relationship. More particularly, a firstalgorithm 102 (algorithm “J”) is used as input for a second algorithm102 (algorithm “K”), where both J and K further rely on “original” data(such as data obtained directly from the one or more data sources). FIG.1B illustrates a second dependency graph 150 including more complexinterdependencies between algorithms. The second dependency graph 150includes a first algorithm 152 (algorithm “G”), a second algorithm 154(algorithm “H”), a third algorithm 156 (algorithm “I”), and a fourthalgorithm (algorithm “X”) that have a hierarchical structure in whichalgorithm 1156 relies on outputs from each of algorithm G 152 andalgorithm H 154, and algorithm X 158 relies on output from each ofalgorithms H 154 and I 156. Each of algorithm G 152, algorithm H 154,and algorithm X 158 also rely on original data. In light of thedependencies between the algorithms 152-158, execution of algorithm X158 requires that algorithms G 152, H 154, and 1156 be previouslyexecuted in order. Specifically, while algorithm G 152 and algorithm H154 may be executed in any order (including in parallel), each ofalgorithm G 152 and algorithm H 154 must be executed prior to executionof algorithm 1156, which in turn must be executed prior to execution ofalgorithm X 158.

The dependency relationships illustrated in FIGS. 1A and 1B result in asubstantially linear flow of data between the algorithms such that eachalgorithm depends only on outputs produced by “upstream” algorithms.However, relationships between algorithms may also be circular orrecursive. For example, a first algorithm may be dependent on the outputof a second algorithm that is also dependent on the output of the firstalgorithm. In such instances, a change to an input of either the firstalgorithm or the second algorithm may result in repeated execution ofthe algorithms as their outputs are continuously updated. To address thepossibility of such a loop occurring infinitely, the outputs ofrecursive algorithms may be considered final when, among other things, apredetermined number of loop executions have occurred or the algorithmoutputs sufficiently converge on a particular value, such as when thechange in the outputs between subsequent executions of the algorithmsfall below a predetermined threshold.

In light of these types of data consistency issues, systems and methodsin accordance with this disclosure rely on a dependency graph thatestablishes relationships between algorithms of the algorithmcollection. In certain implementations, for example, the algorithmcollection is maintained as a graph database in which each noderepresents an algorithm of the algorithm collection and each branchrepresents a dependency between algorithms. Accordingly, when datarelied upon by the system changes, a processing or execution order maybe established by traversing the algorithm collection starting at nodescorresponding to algorithms that rely directly on the changed data. Thegenerated processing order, which may be implemented as an executionplan executable by the system, preserves data consistency by ensuringthat an algorithm is executed only after all algorithms on which thealgorithm depends have been executed. For example, a first algorithmthat uses the output of one or more second algorithms as inputs willgenerally be placed within the processing order and execution plan suchthat it is executed after the one or more second algorithms. Notably,the execution plan generally includes only those algorithms of thealgorithm collection that are either directly or indirectly dependent onthe changed data and, therefore, represents the minimum set ofalgorithms required to be executed in order to propagate changed datathrough the system. To the extent algorithms of the execution planrequire additional data, such data can be readily retrieved from adatabase of current values, such as the tier 1 database withoutperforming any additional calculations or executing any additionalalgorithms. As a result, changes to data relied upon by the system canbe propagated through the system with a minimal amount of computationalresources and time.

As previously noted, systems in accordance with this disclosure may beused to generate a score or other metric for evaluating options, such asbusiness opportunities, based on the data available to the system. Suchevaluations may be presented to a user through a user interfaceincluding, without limitation, a program executable by a user computingdevice, a web portal accessible through a web browser, or any similarsoftware for retrieving and presenting data to a user. In certainimplementations, the user interface may also present informationregarding the underlying methodology for calculating a score or allow auser to generate or modify such methodologies. For example, a user maymodify weights assigned to different algorithm results or data valuescorresponding to metrics or predictors used in generating a score. Inthe context of evaluating business opportunities, such metrics mayinclude, but are not limited to, churn prediction, ease to capture,potential profit, scope of product needs, and scores for specificproducts. In addition to relying on direct input from a user, the systemmay also be configured to modify the evaluation process based oncharacteristics of the business being evaluated. For example, the systemmay modify the metrics considered and their relative weightings basedon, among other things, the type of organization being evaluated, thesize of the organization, specific needs or considerations of the user,and the processing power required to perform underlying calculations.

The user interface may also allow a user to provide feedback valuescorresponding to data used in performing a particular evaluation. Forexample, a user may notice that the system is relying on outdated dataregarding the employee count of a particular customer. The user may thenuse the user interface to provide an updated employee count value thatis then processed by the system. Similar to changes identified in datastored in the data sources, such processing may generally includestoring the feedback value and identifying and executing only thosealgorithms that are dependent on the feedback value. Accordingly,propagating user feedback through the system is also achieved in a rapidmanner that preserves computational resources.

Referring to the type of entity, for example, the entity type may be,but is not limited to, one of an individual customer, a potentialclient, or a business opportunity. In one implementation, the entity maybe an individual customer of a grocery store implementing a system inaccordance with this disclosure. In such an implementation, the dataevaluated by the system may include, without limitation, grocery itemspurchased by the customer, coupons used by the customer, the time of day(or day of week) of the customer's shopping experiences, which registerwas used for one or more transactions, brand preferences of thecustomer, and amounts of goods purchased during one or moretransactions. In the case of customers who are companies or businessopportunities, the data may include, without limitation, a size of thecompany, past buying behavior of the company, current services providedto the company, competitors of the company, and a quantification ofmanpower interactions with the company (such as the number of hoursspent dealing with the customer on a weekly basis).

As previously noted, the underlying data required for evaluations andscoring may be obtained or otherwise received from one or more datasources, each data source providing a respective portion of theunderlying data. To facilitate retrieval of data from the data sources,harvester applications or modules may be implemented to harvest specificpoints of data from respective data sources. Each data harvester maymonitor or otherwise receive notifications of new or modified data in acorresponding data source and, when such changes occur, initiatepropagation of the changes through the system. For example, theharvesters may be configured to generate notification messages includingthe changed data and to transmit such notifications to a central moduleor application of the system that then handles the notificationaccordingly. By generating and transmitting notifications in response tonew or updated data, the data harvesters reduce the frequency and amountof data that is imported from the data sources and eliminatesunnecessary importation and processing of unchanged data, therebyconserving network bandwidth and computational resources and improvingthe speed at which changes to the data sources are propagated throughthe system. Handling the notification may include, among other things,coordinating the generation and execution of an execution plan thatre-runs any algorithms that are dependent (directly or indirectly) onthe changed data in an order that maintains data consistency andupdating any corresponding entries maintained in the tier 1 database. Incertain implementations, updates to the data sources and, morespecifically, corresponding change notifications may be received inreal-time, effectively causing the system to re-execute any algorithmsrelying on the changed data in real-time. Accordingly, if a score orsimilar metric for a particular option or opportunity is maintained bythe system, the score will also be updated in real-time to reflect theupdated data.

In certain implementations, the tier 1 database or portions thereof mayalso be cached to further facilitate ease and speed of access to thedata maintained therein. Such caches may be configured to storeimportant or frequently accessed data of the tier 1 database in a morereadily accessible location or format or on a higher performance datastorage system to improve access speeds. In certain implementations,caches may be used to store portions of the tier 1 database for use byparticular departments or business units within an organization.Accordingly, each cache may include data of the tier 1 database that isparticularly relevant or otherwise frequently accessed by a respectiveunit of the organization. Implementation of such caches generallyimproves responsiveness to requests received from users for data thatwould otherwise be retrieved from the tier 1 database and reduces theoverall load placed on the tier 1 database. To the extent a cache ismaintained locally relative to the primary users of the cache, cachingmay also reduce network traffic and associated costs for retrieving thedata.

Scores and other evaluations generated by systems described herein maybe displayed or otherwise provided to a user via a graphical userinterface, email, text message, and/or any other communicationmechanism. The user may also be provided with the underlying data orresults of algorithms relied upon in calculating the score. For example,if the score of a business opportunity or customer suddenly dropsbecause a particular algorithm generated an output indicating sub-parperformance, the score email sent to the user may include the updatedscore with an explanation that the score dropped as a result of aparticular factor used in the scoring process. In such circumstances,the entity could also be flagged or otherwise noted as not performingwell, and the flag can likewise be communicated to the user via email,text, or a notification on the graphical user interface.

The system may use any number of algorithms to determine the score of aparticular option or opportunity. In one example, a customer may beevaluated based on a number of received complaint tickets originatingfrom the customer. Such an evaluation may include algorithms directed toone or more of aggregating actual complaint ticket data from one or moredata sources, generating a predicted number of complaint tickets basedon mathematical models, and producing a score based on one or more of anabsolute number of complaint tickets received from the customer, apredicted number of complaint tickets, and a comparison between theabsolute and predicted number of complaint tickets. The predicted numberof complaint tickets may further include executing of one or morealgorithms that model customer complaint activity based on historicalcustomer data, the size of the customer, the type of products/servicesbeing provided to the customer, the behavior of similar customers, andother factors. During operation, the evaluation may be executed aninitial time and the results of each algorithm involved may be stored ina database, such as the tier 1 database. If the system subsequentlyreceives a notification that any data upon which the algorithms used toproduce the actual or predicted number of complaint tickets has changed,the system may re-execute only those algorithms required to perform thespecific complaint ticket analysis. In certain implementations,evaluations may be based on changes in scores, data, or metrics derivedfrom such data over time. So, for example, the foregoing evaluation mayconsider changes in the number of received customer complaint ticketsover time or changes in the overall score assigned to the customer.

The system may be configured to generate a notification when the resultsof an evaluation exceed or fall below a predetermined threshold orvalue. In the context of complaint tickets, for example, a notificationmay be generated when the actual number of complaint tickets receivedfrom a customer exceeds the predicted number of tickets by more than apredetermined amount, such as 1.5 standard deviations from the predictedamount. A similar notification could be produced when the number oftickets issued is less than 1.5 standard deviations from the predictedamount. The use of 1.5 standard deviations is exemplary only—thethreshold variance can vary from circumstance to circumstance andconfiguration to configuration, and need not be based on standarddeviation. Any threshold difference from a predicted value can be usedto identify outliers. In addition, this data can be used to identifytrends over predetermined durations of time (days, weeks, months, etc.).

In certain implementations, some or all of the algorithms of thealgorithm collection used to evaluate an option or opportunity may beproprietary and may not be directly modified by a user of the system. Inother implementations, the algorithms may be created or modified by auser of the system based on their particular needs. In either case,changes to the algorithm collection or one or more algorithms may causethe system to re-execute the modified algorithms and any algorithmsdepending therefrom. More specifically, in response to adding a newalgorithm into the algorithm collection or modifying an existingalgorithm of the algorithm collection, the system may generate anexecution plan in which the new/modified algorithm and any algorithmsdepending therefrom are executed in an order that preserves dataconsistency.

In summary, systems and methods according to the present disclosurefacilitate efficient analysis of large quantities of data for use invarious applications, such as analysis of business opportunities. Thequantity of data and calculations required for such analyses lead tosignificant inefficiencies in known systems, which generally retrieveall relevant data and execute a full suite of algorithms required by theanalysis. In contrast, systems disclosed herein include a uniquearchitecture that enable a faster and more efficient approach in whichonly changed data is imported and only algorithms dependent on changeddata are executed. More specifically, the implementation of dataharvesters that monitor and report changes in data sources relied uponby the system and the maintenance of a central tier 1 database ensuresthat only changed data is imported into the system. Further, the use ofan algorithm collection and dependency data regarding the algorithms ofthe collection enables the generation of an execution plan forpropagating changes through the system in which only algorithmsdependent on changed data are included. By doing so, the amount of dataimported into the system and the quantity of algorithms executed toevaluate such data are optimized, reducing the computational workload,network bandwidth, time, and overall costs associated with maintainingthe data system.

As an example of use of methods and systems disclosed herein, supposeCompany A (or, more specifically, a sales employee of Company A) wishesto evaluate the relative strength of pursuing business opportunitieswith Company X, who is an existing customer of Company A. One or moredata harvesters may be implemented by Company A to retrieve, normalize,and process data regarding company X, such as sales figures, number ofbilling disputes, cost of resolving billing disputes, and types ofproducts sold, that has been collected in data sources maintained byCompany A. Company A may also wish to use additional data from externalsources to evaluate Company X. To do so, Company A may implementadditional data harvesters to retrieve and process data from externaldata sources such as those operated by SALESFORCE, LINKEDIN, DATA.COM,DUN & BRADSTREET, OCEAN INFORMATICS, and similar data vendors. Theadditional data harvesters may obtain additional data about Company X,thereby allowing Company A to build a large amount of reference dataabout Company X. Such reference data may include, but is not limited to,customer/target firmographics, current services, competitor buildinglists, intent data, buying behaviors, and other data with which CompanyA may evaluate Company X.

The system of Company A includes or otherwise has access to a collectionof algorithms that may be used to evaluate Company X. The algorithmcollection may include proprietary algorithms purchased or otherwiseacquired from third-parties as well as custom algorithms developed byCompany A. In general, each algorithm of the algorithm collectionrequires input which may be data values from the data sources accessibleto Company A or the results of other algorithms of the algorithmcollection. Such algorithms may include those used to determine, withoutlimitation, one or more of a churn prediction, ease to capture,potential profit, a tier 3 sweet spot, total spending, specific productscores, and scope of product needs. As a preliminary step, Company A mayretrieve, via the data harvesters, an initial set of data from the datasources to at least partially populate a database of current datavalues, also referred to herein as a “tier 1” database. After initialdata collection, an evaluation of Company X may be initiated in whichalgorithms of the algorithm collection are executed in a processingorder that is dynamically generated to preserve data consistency. Theevaluation may culminate in a score or similar metric assigned toCompany X which may also be stored in the tier 1 database. To the extentcalculation of Company X's score includes execution of multipleunderlying algorithms, the results of the underlying algorithms may alsobe stored within the tier 1 database.

After the initial evaluation of Company X, the data harvesters maymonitor their respective data sources for any changes or additions. Whensuch a change is identified, the system generates and executes a newexecution plan such that the system re-runs any algorithms that aredirectly or indirectly dependent on the changed data and updates entriesof the tier 1 database, including Company X's score, accordingly.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

FIG. 2 is a schematic illustration of a data system 200 according to anembodiment of the present disclosure. To further illustrate operationsperformed by the data system 200 and components thereof, reference isalso made to an example method 300 for maintaining data in a datasystem, such as the data system 200, illustrated in FIGS. 3A and 3B andwhich is discussed in further detail later in this disclosure.

To provide further context to the following discussion, a basic exampleof a spend analysis will be used to illustrate a possible application ofthe systems and methods disclosed herein. In the example, a sales teamfor a telecommunications provider uses the system to identify potentialbusiness opportunities related to products and services offered by thetelecommunications provider. The example application is intended only toillustrate systems and methods disclosed herein. Accordingly, it is notintended to limit the type of data analyzed by the data system 200, thepotential applications of the data system 200, or any other similaraspect of this disclosure.

For purposes of the example, the telecommunications provider may beinterested in evaluating customers based on a predicted or potentialtotal spending on voice services as compared to an existing amount ofthe services currently being provided to the customer. Such services mayinclude, without limitation, voice over Internet protocol (VoIP)services, trunking services, long distance calling, toll free calling,call center services, and other business-related telephony products andservices. As a result of the analysis, the customer may be assigned ascore based on, among other things, a dollar value of potential businesswith the customer or percentage of total spending already captured. Eachcustomer may also be assigned a score or rank based on its relativepotential as compared to other customers being monitored by thetelecommunications provider.

The data system 200 facilitates consolidation and analysis of datacollected from multiple data sources, such as data sources 202-208. Thedata sources 202-208 generally store customer information and otherbusiness-related data. The data sources 202-208 may include, withoutlimitation, databases (such as relational databases) and flat files(such as spreadsheets). For example, one or more of the data sources202-208 may be a data warehouse or data mart provided by a third-partyand accessible through the data system 200.

Each of the data sources 202-208 communicates with a data service orapplication 222 through respective data harvesters 210-216. Data system200 further includes a feedback data harvester 218 corresponding to afeedback system 240, which is discussed in more detail, below.

Each of the data harvesters 210-216 are configured to monitor, identifychanges, and generate data source change notifications corresponding toa respective one of the data sources 202-208. Similarly, the feedbackdata source 218 is configured to monitor, identify changes, and generatedata source change notifications based on modifications to a feedbackdatabase 242. For purposes of clarity, the following description refersto the data source 202 and the data harvester 210 to explain thefunctionality of the data harvesters within data system 200.Accordingly, and unless otherwise noted, the following descriptiongenerally applies to each of data harvesters 210-216 and feedback dataharvester 218.

The data harvester 210 monitors the data source 202 and identifieschanges to values of one or more data fields maintained in the datasource 202. In certain implementations, the data harvester 210 isconfigured to check for changes to the data source 202 according to apredetermined schedule (e.g., daily, weekly, monthly). In suchimplementations, the data harvester 210 may identify changes based on a“last updated” or similar time stamp indicating the last time data hadbeen modified and, more particularly, whether data had been modifiedsince the last check performed by the data harvester 210.

In the context of the example application, the telecommunicationsprovider may implement the data system 200 in order to consolidate andanalyze various customer data sources to which the provider has access.Such data sources may include those provided by third-party market datavendors but may also include internal customer data including, withoutlimitation, sales information, billing data, contact information,product and service specifications, and any other relevant datamaintained by the provider.

In response to identifying a change in the data source 202, the dataharvester 210 generates a data source change notification fortransmission to the data application 222. The data source changenotification may include the changed data value and other identifyinginformation including, without limitation, a field name, a field type, afield size, and a modification date/time. Generation of the data sourcechange notification may include normalization of the data including,without limitation, changing one or more of a field length, a fieldtype, a field name or any other data parameter such that the changeddata retrieved from the data source 202 conforms to system standards.

In certain implementations, the data harvester 210 may generate data foruse in the data system 200 by aggregating or otherwise performingcalculations based on data of the data source 202. For example, the datasource 202 may store revenue data on a monthly basis and the dataharvester 210 may be configured to generate quarterly revenue data.Accordingly, the data harvester 210 may retrieve monthly revenue datafor the past quarter and aggregate the monthly revenue data fortransmission to the data application 222. More specifically, the dataharvester 210 may retrieve monthly revenue data for the past quarter,sum the monthly revenue data, and generate a data source changenotification having a quarterly revenue data field that is thentransmitted to the data application 222.

In the example application, for instance, the telecommunicationsprovider may evaluate actual spend based on internally maintainedrevenue data and predict spend based on the number of customeremployees. In the first example, the telecommunications provider maymaintain revenues for products and services sold to the customer in adata source by product/service. Accordingly, the data harvester may beconfigured to aggregate all revenue data associated with the customerinto a single value. Similarly, the telecommunications provider mayobtain employee headcount data from a third-party data vendor thatmaintains such data by state and may implement a data harvester adaptedto collect and consolidate employee headcount data to obtain a nationalor region-specific number.

To the extent the data harvester 210 relies on multiple data fields ofthe data source 202 to derive data for use by the data system 200, thedata harvester 210 may monitor each relevant data field of the datasource 202 for changes and generate data source change notifications inresponse to changes to any of the relevant data fields. Referring to theemployee headcount example, if the data source 202 includes employeeheadcounts on a state-by-state basis, the data harvester 210 may beconfigured to aggregate state employee headcounts into a nationalemployee headcount value. The data harvester 210 may monitor each stateemployee headcount field of the data source 202 and generate a datasource change notification when any of the state employee headcountfields changes. More specifically, in response to identifying a changein any of the state employee headcount fields, the data harvester 210may aggregate all state employee headcount fields (including any updatedfields) into a national headcount data value and generate a source datachange notification including a national headcount data field with theupdated value. The source data change notification may then betransmitted to the data application 222 for processing.

The foregoing process of identify changes in data sources and generatingnotifications in response to such changes are generally illustrated byoperations 310 and 312 of the method illustrated in FIG. 3A. Notably,because the data harvester 210 is adapted to generate and transmitchange notifications only in response to changes in the data source 202,the time and bandwidth required to retrieve data from the data source202 is significantly reduced as compared to known systems in which datais retrieved in bulk and regardless of whether the data has beenmodified.

Data source change notifications transmitted by the data harvesters210-218 are received and processed by the data application 222. Incertain implementations, data source change notifications are maintainedin a message queue 220 and processed sequentially by the dataapplication 222. In response to receiving a data source changenotification and as illustrated by operation 314 of FIG. 3A, the dataapplication 222 updates each of a tier 1 database 226 and a tier 2database 224.

The tier 1 database 226 stores only the most up-to-date values of datafields relevant to customer analyses performed by the data system 200.Such data fields may include data obtained from the data sources202-208, and 242, as well as results generated by executing variousalgorithms for analyzing such data, as described in more detail below.In certain implementations, the tier 1 database 226 may be a graphdatabase that includes parallel sets of data fields and scorescorresponding to each customer.

By storing current values used by the data system 200 in the tier 1database 226, the need to retrieve data and re-execute algorithms by thedata system 200 is significantly reduced. More specifically, when achange notification is received by the data application 222, the dataapplication 222 may retrieve any unchanged data or unchanged algorithmresults (i.e., results of algorithms that are not dependent on thechanged data) required to propagate the changed data through the datasystem 200 from the tier 1 database. By doing so, the resources (e.g.,bandwidth, computational power, costs, etc.) that would otherwise beconsumed by retrieving the data from a corresponding data source orre-executing an algorithm are preserved and the overall time required topropagate the changed data is improved.

The tier 2 database 224 generally stores historical data regardingchanges to the tier 1 database 226. Accordingly, in response toreceiving a data source change notification from one of the dataharvesters 210-218, the data application 222 may generate a record inthe tier 2 database 224 including data and characteristics of the datasource change notification. For example, in certain implementations, thedata application 222 may generate a record in the tier 2 database 224indicating one or more of an origin of the data source changenotification, the updated value included in the data source changenotification, the original value of the field that was changed, adate/time of the change, and any other data that may be associated withthe data source change notification.

In certain implementations, the data application 222 only updates datavalues in the tier 1 database 226 after first determining that a datasource change notification received from one of the data harvesters210-218 actually modifies one of the data fields maintained in the tier1 database 226. For example, the data application 222 may receive a datasource change notification indicating a change in a time stampassociated with a particular data field of one of the data sources202-208 and 242. Before modifying the tier 1 database 226, the dataapplication 222 may first determine what change has occurred and whetherthe change is to an actual data value or simply to information relatedto the data field corresponding to the data value. To do so, the dataapplication 222 may retrieve the most recent record associated with thedata field from the tier 2 database 224 and determine what changes haveactually occurred. To the extent the data value in the tier 1 database226 has not been modified, the data application will not perform anymodifications on the tier 1 database 226.

After receiving and processing a data source change notification and asillustrated by operation 316 of FIG. 3A, the data application 222transmits a data value change notification to an algorithm manager 228.The algorithm manager 228 generally coordinates execution of one or morealgorithms of an algorithm collection 230 in response to a data valuechange in the tier 1 database 226. Algorithms of the algorithmcollection 230 may be configured to generate an output that is stored inthe tier 1 database 226 and subsequently used to evaluate businessopportunities. The output of an algorithm may also be used as an inputto a second algorithm. As a result, an algorithm may be dependent on theresult of one or more other algorithms of the algorithm collection 230.Accordingly, to maintain data consistency, the system may executealgorithms in a particular order dictated by algorithm dependencies.

In response to receiving a data value change notification and asillustrated by operation 318 of FIG. 3A, the algorithm manager 228submits an execution plan request to an algorithm metadata service 232.The execution plan request may include, among other things, anidentifier of the changed data field along with the new value or a valuerepresentative of a difference between the new value and a previousvalue. The algorithm metadata service 232 then generates an executionplan that provides the specific order by which algorithms of thealgorithm collection 230 are to be executed in order to maintain dataconsistency.

To generate the execution plan, the algorithm metadata service 232communicates with and receives data from an algorithm metadata database234. The algorithm metadata database 234 includes entries for eachalgorithm of the algorithm collection 230 and their respectivedependencies. A “dependency” of a given algorithm is any algorithm thatrequires the result of the given algorithm (or another dependentalgorithm of the given algorithm) as an input. In certainimplementations, entries of the algorithm metadata database 234corresponding to a given algorithm may include a full list of dependentalgorithms or may include only a first level of dependent algorithmsfrom which a full list of dependencies may be identified by recursivelytraversing the metadata database 234. Similar to the tier 1 database226, the algorithm metadata database 234 may be a graph database witheach algorithm corresponding to a node in the database and eachdependency corresponding to a branch between dependent algorithms. Eachentry in the algorithm metadata database 234 may further include a listof data fields of the tier 1 database 226 on which the algorithmcorresponding to the entry depends.

During operation, the algorithm metadata service 232 identifiesalgorithms affected by data changes in the data sources 202-208. Morespecifically, the algorithm metadata service 232 traverses the algorithmmetadata database 234 using the changed data field identified in theexecution plan request to identify which algorithms of the algorithmcollection 230 are affected by the changed data field and the order inwhich the identified algorithms are to be executed to ensure dataconsistency. In other words, the changed data field included in theexecution plan request is used as a search term or key to identifyalgorithms of the algorithm collection 230 that directly rely on thevalue stored in the changed data field. In one implementation, forexample, the algorithm metadata service 232 traverses the algorithmmetadata database 234 and determines the maximum depth of each algorithmaffected by the data field change. To do so, the algorithm metadataservice 232 may identify one or more algorithms that depend directlyfrom the changed data field (and possibly addition data field valuesstored in the tier 1 database 226) and treats each such algorithm as a“root” algorithm. The depth of each algorithm that depends from the oneor more root algorithms may then be ascertained. For purposes of thisdisclosure, the depth of an algorithm generally corresponds to thenumber of traversals or distance between the root algorithm and thealgorithm in question. Accordingly, an algorithm that depends on datathat is dependent on only the root algorithm may be assigned a depth of“1,” while an algorithm that depends on data from an intermediatealgorithm may be assigned a depth of “2,” and so on. Certain algorithmsmay have multiple depth values due to multiple dependency “paths”existing between the root algorithm and the algorithm in question. Insuch cases, the metadata service 232 identifies the maximum depth ofeach algorithm.

After the maximum depth of each algorithm is ascertained, an executionplan is generated in which each algorithm having the same maximum depthis grouped in a corresponding “layer” of the execution plan. Becauseeach layer of the execution plan includes algorithms having the samemaximum depth, each algorithm within a given layer can be executed inany order, including in parallel, without compromising data consistencywithin the data system 200. An example process of generating anexecution plan in accordance with the foregoing is illustrated byoperations 320-328 of FIG. 3B. Notably, the execution plan generated bythe metadata service will only include algorithms of the algorithmcollection 230 that are dependent (either directly or indirectly) on thechanged data identified in the execution plan request. In other words,the execution plan will exclude any algorithms of the algorithmcollection 230 that are unnecessary to run to propagate the changed datathrough the data system 200. As a result, execution of the algorithmsidentified in the execution plan generally includes executing theminimum number of algorithms and consuming the least amount ofcomputational resources required to propagate changes through the datasystem 200 and to update the data maintained in the tier 1 database 226.

After the algorithm metadata service 232 generates an execution plan,the execution plan is sent to the algorithm manager 228 forimplementation as illustrated in operation 330 of FIG. 3B. In thelayered execution plan discussed above, for example, the algorithmmanager 228 would execute the algorithms identified in each successivelayer of the execution plan. Execution of a given algorithm generallyincludes the algorithm manager 228 submitting a data request to the dataapplication 222 for any required data. The required data is generallystored in the tier 1 database 226 and, as a result, can includenormalized data received from data sources (such as data sources 202-208and feedback database 242) or scores generated by previously executedalgorithms. In response to receiving the required data, the algorithmmanager 228 executes the current algorithm, thereby producing a currentalgorithm score, and transmits the current algorithm score to the dataapplication 222 for storage in the tier 1 database 226. The process ofrequesting and receiving data, executing the current algorithm, andtransmitting the score generated by the current algorithm to the dataapplication 222 for storage in the tier 1 database 226 is then repeatedfor each algorithm in the current layer of the execution plan.

After the algorithm manager 228 executes the algorithms in a given layerof the execution plan, the foregoing process is repeated for subsequentlayers of the execution plan until all layers of the execution plan havebeen executed. As previously noted, each layer of the execution planincludes algorithms having the same maximum depth. By executing theexecution plan in a layer-by-layer manner, data consistency ismaintained because any antecedent algorithms from which a givenalgorithm depends are necessarily in a higher (i.e., earlier executed)layer. As a result, any antecedent algorithm outputs used as inputs by aparticular algorithm are updated prior to execution of the algorithm.The general process by which the algorithm manager 228 executes theexecution plan and updates the tier 1 database is illustrated inoperations 332-346 of FIG. 3A.

The algorithms maintained in the algorithm collection 230 maysignificantly vary in complexity. Certain algorithms may perform basicmathematical operations on data stored in the tier 1 database 226. Forexample, referring back to the employee headcount example, dataharvesters may collect employee count data for individual customer sitesand a “total employee count” algorithm may simply add together theseparate employee counts to generate the total number of customeremployees. The total number of customer employees may then be used as aninput to other algorithms. Algorithms may also correspond to predictivemathematical models. The telecommunications provider of the exampleapplication, for instance, may develop one or more algorithms forpredicting a total spend of a customer. Such a model may be a basiclinear model that multiplies the number of customer employees by apredetermined spend per employee value to obtain a customer's totalspend or may include a more complicated, multi-variable model that takesinto account other parameters including, without limitation, parametersrelated to the customer, the industry in which the customer operates,and behavior of similar customers. Notably, individual coefficients andvalues used by an algorithm of the algorithm collection may in turn bethe result of other algorithms of the algorithm collection. For example,the previously discussed linear model for total customer spend mayreceive inputs from a first algorithm that calculates a total employeeheadcount and a second algorithm that determines average spend peremployee for the industry in which the customer operates.

The data system 200 further includes a user interface 236 for accessingthe data system 200. The user interface 236 may be implemented as a webportal, an application, or similar software and enables a user of theuser interface 236 to perform various tasks associated with the datasystem 200. For example, the user interface 236 may be configured toaccess one or more services 238 for accessing data of the consumer datasystem 200, performing analytics, performing maintenance and/ortroubleshooting operations, and the like. The services 238 also providea primary integration point for any external systems that maycommunicate with the consumer data system 200.

As a first example, the user interface 236 facilitates analysis andmaintenance of the data system 200. For example, an informationtechnology group of an organization may access the data system 200through the user interface 236 to update data stored in one or more ofthe tier 2 database 224 and the tier 1 database 226. The user interface236 may also be used to add to, delete from, or otherwise modify thealgorithm collection 230 and to make any corresponding changes to thealgorithm metadata database 234.

As another example, the user interface 236 may be used by a salespersonor similar user to access data and identify potential businessopportunities. For example, in some implementations the user interface236 accepts an inquiry from the user regarding a particular businessopportunity and presents the user with a score or similar metriccorresponding to the relative strength of the opportunity. The userinterface 236 may further provide any other information stored orotherwise derived from data stored in the tier 2 database 224 and/or thetier 1 database 226 related to the business opportunity or to itsscoring.

In certain implementations, the user interface 236 may allow a user todrill down into data underlying a particular customer score. To theextent the user identifies an error in the data, has more recent data,or is otherwise aware of a discrepancy between the data stored in thedata system 200 and actual metrics regarding a given customer, the usercan submit feedback to a feedback system 240. The feedback system 240generally provides a mechanism for receiving, evaluating, andimplementing changes to data of the consumer data system 200 based onuser feedback. Accordingly, the feedback system 240 receives feedbacknotifications from the user interface 236 and implements a workflowapplicable to the given feedback notification. For example, the feedbacksystem automatically determines whether particular feedback from theuser interface 236 should be automatically accepted, rejected, orsubjected to further review, such as by generating a ticket or similarnotification for follow up.

In certain implementations, feedback notifications are stored in afeedback database 242 which is in communication with a feedback dataharvester 218. Similar to data harvesters 210-216, the feedback dataharvester 218 monitors the feedback database 242 for any feedback thatincludes a modification to one or more data values maintained in thetier 1 database 226. When such feedback is received, the feedback dataharvester 218 normalizes the feedback data and transmits a feedbacknotification to the data application 222 via the message queue 220. Thedata application 222 then processes the feedback notification by addingthe feedback data to the tier 1 database 226 and transmits acorresponding data value change notification to the algorithm manager228. The algorithm manager 228 then proceeds with obtaining andexecuting a corresponding execution plan in order to propagate anychanges resulting from the feedback through the consumer data system200.

In certain implementations, data value changes received as feedback aredirectly used as inputs to algorithms during execution. In otherexamples, data value changes received as feedback are used to weigh orotherwise modify existing values for the given data value previouslyobtained from one of the data sources 202-208 and already stored in thetier 1 database 226. For example, a user may notice that the consumerdata system 200 believes a company headcount to be 475 employees whilethe user has reliable information that the headcount is actually 575.The user may then provide corresponding feedback via the user interface236 indicating the discrepancy. During propagation of the subsequentupdate to the headcount value, the algorithm manager 228 coordinatesexecution of algorithms of the algorithm collection 230 which requireheadcount as an input. Accordingly, the algorithm manager 228 may beconfigured to provide the feedback value (575) or some combination ofthe current tier 1 value (475) and the feedback value. For example, thealgorithm manager may use an average of the feedback value and thecurrent tier 1 value, a weighted average of the feedback value and thecurrent tier 1 value, or any similar combination of the feedback valueand the current tier 1 value as input to a given algorithm. Inimplementations in which a weighted average or similar weightedcombination is used, the weightings assigned to each of the feedbackvalue and the tier 1 value may vary over time. For example, a feedbackvalue may initially be assigned a 50% weighting that gradually decreasesover time until only the current tier 1 value is used.

The process of retrieving and combining a feedback value and a currenttier 1 value can also be triggered by modification of a data value otherthan that corresponding to the feedback value. For example, an algorithmthat determines average spend per employee may require each of a totalspend value and a total employee headcount value as inputs. If the totalspend value is later modified, the average spend per employee algorithmwould be executed as described herein. If feedback had been previouslyprovided regarding the total employee value, the algorithm manager 228would retrieve the updated total spend value as well as each of thecurrent tier 1 employee count value and the feedback employee countvalue. The employee count values may then be combined as previouslydiscussed to generate an input for the algorithm.

FIGS. 3A-3B depict a flow chart illustrating an example method 300 formaintaining customer data in a data system, such as the data system 200of FIG. 2. With reference to the computer data system 200 of FIG. 2,FIGS. 3A-3B include boxes 302, 304, 306, and 308, which correspond toelements of the data system 200 and contain flow chart elementscorresponding to operations that may be executed by the elements. In theexample method 300, box 302 corresponds to the data harvester 210, box304 corresponds to the data application 222, box 304 corresponds to thealgorithm manager 228, and box 306 corresponds to the algorithm metadataservice 232.

Referring first to box 302, the data harvester 210 identifies a changeto a data value in a corresponding data source, such as the data source202 of FIG. 2 (operation 310). In response to identifying the change inthe data value, the data harvester 210 generates a data source changenotification and transmits the data source change notification to thedata application 222 (operation 312). The data source changenotification generally includes at least a data field or identifier fora data field corresponding to the changed data value of the data source202 as well as the new data value itself. In certain implementations,the process of generating the data source change notification mayinclude normalizing the data, such as by reformatting the data oraggregating multiple data fields of the data source 202 into a singlevalue.

In response to receiving a data source change notification, the dataapplication 222 updates each of the tier 2 database 224 and the tier 1database 226 with the changed data value (operation 314). For example,the data application 222 adds a record to the tier 2 database 224corresponding to the data source change notification. The record addedto the tier 2 database 224 may include, without limitation, the datafield changed, a timestamp associated with receipt of the change, thesource of the data source change notification, the changed data source,and similar information regarding the data. With respect to the tier 1database 226, the data application 222 adds or updates a value of thetier 1 database 226 corresponding to the data field of the data source202 to reflect the change. The tier 1 database 226 is preferablymaintained as a graph database that contains only the most current datavalues for data used by algorithms of the data system 200. Inimplementations in which algorithms are dependent on each other, thetier 1 database 226 may also store scores or other results of algorithmsthat may be used as inputs for dependent algorithms.

After the tier 2 database 224 and the tier 1 database 226 have beenupdated, the data application 222 generates a data value changenotification and transmits the data value change notification to thealgorithm manager 228 (operation 316). The data value changenotification includes the data field or a corresponding identifier ofthe data field that was changed. In response to receiving the data valuechange notification, the algorithm manager 228 generates and transmitsan execution plan containing the changed data field to the algorithmmetadata service 232 (operation 318; box 308, which corresponds to thealgorithm metadata service 232, is shown in FIG. 3B).

The algorithm metadata service 232 is configured to identify allalgorithms dependent, either directly or indirectly, on the changeddata. More specifically, the algorithm metadata service 232 is incommunication with an algorithm metadata database 234 that containsinformation regarding the inputs and dependencies of each algorithm ofan algorithm collection 230 used by the data system 200 to evaluatebusiness opportunities. The algorithm metadata database 234 ispreferably a graph database with each node corresponding to an algorithmof the algorithm collection 230 and each branch representingdependencies between the algorithms.

When the algorithm metadata service 232 receives the execution planrequest, it identifies each algorithm that would be affected by a changeto the data field included in the execution plan request. Morespecifically, the algorithm metadata service 232 uses the data field toidentify each algorithm of the algorithm collection 230 that dependsdirectly on the data field and traverses the algorithm metadata database234 to identify all other dependent algorithms (operation 320). Afterall relevant algorithms have been identified the algorithm metadataservice 232 determines the order in which the relevant algorithms are tobe executed. In the method 300, for example, the algorithm metadataservice 232 determines the maximum depth of each relevant algorithm. Todo so, the algorithm metadata service 232 may initialize an indexcorresponding to a current algorithm (operation 322) and execute a loopin which it is determined if the current index exceeds the maximumalgorithm count (operation 324) and, if not, the maximum depth of thecurrent algorithm is determined (operation 326) and the index isincremented (operation 328). After the algorithm metadata service 232determines the depth of each algorithm, it generates an execution planand transmits the execution plan back to the algorithm manager 228(operation 330). In implementations in which the depth of each relevantalgorithm is used to determine execution order, for example, theexecution plan generated by the algorithm metadata service 232 includesmultiple layers, each of which includes references to algorithms havingthe same maximum depth. To maintain data consistency, the layers areexecuted successively from shallowest to deepest depth where executionof a given layer entails executing each algorithm within the layer.Notably, because the algorithms within a given layer are not dependenton each other, they may be executed in series or in parallel withoutaffecting data consistency.

Referring back to FIG. 3A, the algorithm manager 306 receives andexecutes the execution plan (illustrated as a loop including operations332-348). For example, in the method 300, an index is initialized totrack the current layer of the execution plan (operation 332). A checkis then performed to determine whether the current execution plan levelexceeds the maximum execution plan level (operation 334). If not, thealgorithm manager 306 transmits one or more data requests to the dataapplication 222 for data required by the algorithms of the executionlayer (operation 336). The data application 222 retrieves the requesteddata from the tier 1 database 226 and transmits the data to thealgorithm manager 228 (operations 338 and 340, respectively). Inresponse to receiving the requested data, the algorithm manager 228coordinates execution of each algorithm within the current executionplan layer using the retrieved data (operation 342) and transmits theresults of executing the algorithms to the data application 222(operation 344) for storage in the tier 1 database 226 (operations 346).The execution plan layer index is then incremented (operation 348) andthe process of retrieving data for the current execution plan layer,executing the algorithms of the current execution plan layer, andstoring the corresponding results is then repeated for the next layer ofthe execution plan.

With reference to FIG. 4, an exemplary system includes a general-purposecomputing device 400 including a processing unit (CPU or processor) 420and a system bus 410 that couples various system components includingthe system memory 430 such as read only memory (ROM) 440 and randomaccess memory (RAM) 450 to the processor 420. The system 400 can includea cache 422 of high speed memory connected directly with, in closeproximity to, or integrated as part of the processor 420. The system 400copies data from the memory 430 and/or the storage device 460 to thecache 422 for quick access by the processor 420. In this way, the cacheprovides a performance boost that avoids processor 420 delays whilewaiting for data. These and other modules can control or be configuredto control the processor 420 to perform various actions. Other systemmemory 430 may be available for use as well. The memory 430 can includemultiple different types of memory with different performancecharacteristics. It can be appreciated that the disclosure may operateon a computing device 400 with more than one processor 420 or on a groupor cluster of computing devices networked together to provide greaterprocessing capability. The processor 420 can include any general purposeprocessor and a hardware module or software module, such as module 1462, module 2 464, and module 3 466 stored in storage device 460,configured to control the processor 420 as well as a special-purposeprocessor where software instructions are incorporated into the actualprocessor design. The processor 420 may essentially be a completelyself-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

The system bus 410 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 440 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 400, such as during start-up. The computing device 400further includes storage devices 460 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 460 can include software modules 462, 464, 466 forcontrolling the processor 420. Other hardware or software modules arecontemplated. The storage device 460 is connected to the system bus 410by a drive interface. The drives and the associated computer-readablestorage media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputing device 400. In one aspect, a hardware module that performs aparticular function includes the software component stored in a tangiblecomputer-readable storage medium in connection with the necessaryhardware components, such as the processor 420, bus 410, display 470,and so forth, to carry out the function. In another aspect, the systemcan use a processor and computer-readable storage medium to storeinstructions which, when executed by the processor, cause the processorto perform a method or other specific actions. The basic components andappropriate variations are contemplated depending on the type of device,such as whether the device 400 is a small, handheld computing device, adesktop computer, or a computer server.

Although the exemplary embodiment described herein employs the hard disk460, other types of computer-readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs) 450, and read only memory (ROM) 440, may also be used in theexemplary operating environment. Tangible computer-readable storagemedia, computer-readable storage devices, or computer-readable memorydevices, expressly exclude media such as transitory waves, energy,carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 400, an inputdevice 490 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 470 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 400. The communications interface 480generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

For clarity of explanation, the illustrative system embodiment ispresented as including individual functional blocks including functionalblocks labeled as a “processor” or processor 420. The functions theseblocks represent may be provided through the use of either shared ordedicated hardware, including, but not limited to, hardware capable ofexecuting software and hardware, such as a processor 420, that ispurpose-built to operate as an equivalent to software executing on ageneral purpose processor. For example the functions of one or moreprocessors presented in FIG. 4 may be provided by a single sharedprocessor or multiple processors. (Use of the term “processor” shouldnot be construed to refer exclusively to hardware capable of executingsoftware.) Illustrative embodiments may include microprocessor and/ordigital signal processor (DSP) hardware, read-only memory (ROM) 440 forstoring software performing the operations described below, and randomaccess memory (RAM) 450 for storing results. Very large scaleintegration (VLSI) hardware embodiments, as well as custom VLSIcircuitry in combination with a general purpose DSP circuit, may also beprovided.

The logical operations of the various embodiments are implemented as:(1) a sequence of computer implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits. The system 400 shown in FIG. 4 can practice allor part of the recited methods, can be a part of the recited systems,and/or can operate according to instructions in the recited tangiblecomputer-readable storage media. Such logical operations can beimplemented as modules configured to control the processor 420 toperform particular functions according to the programming of the module.For example, FIG. 4 illustrates three modules Mod1 462, Mod2 464 andMod3 466 which are modules configured to control the processor 420.These modules may be stored on the storage device 460 and loaded intoRAM 450 or memory 430 at runtime or may be stored in othercomputer-readable memory locations.

Where business-related numbers are used in the attached figures orwithin this disclosure (i.e., amounts received, revenues, number oftickets, prices, number of sites, standard deviations, etc.), suchnumbers are provided as non-limiting examples. In other circumstancesthe types of values, how the numbers are used by various analyses, aswell as the amounts of the values, can vary.

Embodiments within the scope of the present disclosure may also includetangible and/or non-transitory computer-readable storage media forcarrying or having computer-executable instructions or data structuresstored thereon. Such tangible computer-readable storage media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer, including the functional design of any special purposeprocessor as described above. By way of example, and not limitation,such tangible computer-readable media can include RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions, data structures, or processor chip design. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or combinationthereof) to a computer, the computer properly views the connection as acomputer-readable medium. Thus, any such connection is properly termed acomputer-readable medium. Combinations of the above should also beincluded within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Other embodiments of the disclosure may be practiced in networkcomputing environments with many types of computer systemconfigurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

We claim:
 1. A method of updating and analyzing data in a data systemcomprising: receiving an algorithm evaluation request; identifying adependency graph indicative of a first dependency of a first algorithmon one or more database values and of a second dependency of a secondalgorithm on the first algorithm based on the algorithm evaluationrequest; identifying, based on the dependency graph, the first algorithmand the second algorithm, wherein identifying the first algorithm andthe second algorithm comprises: transmitting a request including anidentifier corresponding to the one or more database values; andreceiving an execution order in response to the request, the executionorder indicating an order in which to execute the first algorithm andthe second algorithm; and generating an algorithm result of the secondalgorithm by executing each of the first algorithm and the secondalgorithm in accordance with the execution order; and submitting thealgorithm result as a response to the algorithm evaluation request. 2.The method of claim 1, wherein the first algorithm is at least one ofdirectly dependent on the one or more databases value such that thefirst algorithm uses the one or more database values as an input andindirectly dependent on the one or more database values such that thefirst algorithm uses an output of another algorithm dependent on the oneor more database values as an input.
 3. The method of claim 1, furthercomprising storing a result of the first algorithm in the firstdatabase.
 4. The method of claim 1, wherein transmitting the requestcomprises transmitting the request to an application in communicationwith a database including dependency data for a plurality of algorithmsincluding the first algorithm and the second algorithm, the dependencydata indicating inputs required for each algorithm of the plurality ofalgorithms.
 5. The method of claim 4, wherein the database is a graphdatabase.
 6. The method of claim 1, wherein the algorithm evaluationrequest is received from an application in communication with a datasource, the application configured to retrieve and normalize dataobtained from the data source and to generate and transmit the algorithmevaluation request in response to identifying changes to the datasource.
 7. The method of claim 1, further comprising receiving afeedback value corresponding to but different than the one or moredatabase values, wherein generating the algorithm result comprisesproviding an input value to the first algorithm, the input value beingone of the feedback value and a combination of the one or more databasevalues and the feedback value.
 8. The method of claim 7, wherein theinput value is a combination of the one or more database values and thefeedback value, and the input value is one of an average of the one ormore database values and the feedback value, a weighted average of theone or more database values and the feedback value, and a time-dependentweighted average of the one or more database values and the feedbackvalue.
 9. The method of claim 7, further comprising storing the feedbackvalue in a database.
 10. The method of claim 1, wherein the algorithmresult includes a scoring metric.
 11. The method of claim 1, wherein thefirst algorithm and the second algorithm are included in an algorithmcollection including a plurality of algorithms, the method furthercomprising: modifying the algorithm collection by modifying an existingalgorithm of the algorithm collection and adding a new algorithm to thealgorithm collection; executing each algorithm of the algorithmcollection dependent on the modified existing algorithm and the newalgorithm, respectively.
 12. The method of claim 1, wherein theexecution order is generated by: identifying the first algorithm from aplurality of algorithms that are dependent on the one or more databasevalues; assigning the first algorithm to a first execution layer of theexecution order; identifying the second algorithm from the plurality ofalgorithms by traversing a dependency graph including dependency datafor the plurality of algorithms; and assigning the second algorithm to asecond execution layer of the execution order according to a traversaldistance of the second algorithm from the first algorithm, whereinexecuting the first algorithm and the second algorithm in accordancewith the execution order the first execution layer followed by executingthe second execution layer.
 13. A system for organizing and analyzingdata from multiple data sources comprising: an algorithm collectionincluding a plurality of executable algorithms for evaluating data; anda first database including dependency data for each algorithm of theplurality of algorithms; and one or more computing devices configuredto: receive an algorithm evaluation request; identify, based on thefirst database, a first algorithm and a second algorithm of thealgorithm collection, wherein the first algorithm is dependent on one ormore database values, and wherein the second algorithm is dependent onthe first algorithm; execute the first algorithm and the secondalgorithm according to an execution order derived from the dependencydata to generate an algorithm result; and submit the algorithm result inresponse to the algorithm evaluation request.
 14. The system of claim13, wherein the first database is a graph database.
 15. The system ofclaim 13, wherein the one or more computing devices are furtherconfigured to receive the algorithm evaluation request from a dataharvesting application adapted to retrieve and normalize data from adata source.
 16. The system of claim 15, wherein the data harvestingapplication is one of a plurality of data harvesting applications andeach data harvesting application of the plurality of data harvestingapplications adapted to retrieve and normalize data from a respectivedata source, the one or more computing devices further configured toreceive notifications from each of the plurality of data harvestingapplications.
 17. The system of claim 16, wherein the one or morecomputing devices are further configured to: receive an execution planincluding an execution order for the first algorithm and the secondalgorithm, wherein executing the first algorithm and the secondalgorithm includes executing the first algorithm and the secondalgorithm according to the execution order.
 18. The system of claim 17,wherein the one or more computing devices are further configured to:generate, in response to receiving the algorithm evaluation request, arequest for an execution plan; and transmit the request to anapplication in communication with the first database, the applicationadapted to generate the execution plan.
 19. The system of claim 13,wherein the one or more computing devices are further configured togenerate log entries corresponding to data received and analyzed by theone or more computing devices and to store the log entries in a seconddatabase.